House prices report

This document is a data science report of the kaggle house prices tutorial project. It was generated using the Shapash library.

General Information

Version : 0.7

Name : House Prices Prediction Project

Purpose : Predicting the sale price of houses

Date : 2021-11-11

Contributors : Yann Golhen, Sebastien Bidault, Thomas Bouche, Guillaume Vignal, Thibaud Real

Description : This work is a data science project that tries to predict the sale of houses based on 79 explanatory variables. It was designed inside the data science team at X. and improved since the beggining of the project in 2019. The model was put into production since February 2021.

Source Code : https://github.com/MAIF/shapash/tree/master/tutorial

Git Commit : 1ff46e83beafba8949a7f3b7de27586acd6ae99e

---

Dataset Information

Path : https://www.kaggle.com/c/house-prices-advanced-regression-techniques/

Origin : The Assessor’s Office

Description : the sale of individual residential property in Ames, Iowa

Depth : from 2006 to 2010

Perimeter : only residential sales

Target Variable : SalePrice

Target Description : The property's sale price in dollars

---

Data Preparation

Variable Filetring : All variables that required special knowledge or previous calculations for their use were removed

Individual Filtering : only the most recent sales data on any property were kept (for houses that were sold multiple times during this period)

Missing Values : were replaced by 0

Feature Engineering : No feature was created. All features are directly taken from the kaggle dataset. Categorical features were transformed using an ordinal encoder.

Path To Script : https://github.com/MAIF/shapash/tree/master/tutorial/

---

Model Training

Used Algorithm : We used a RandomForestRegressor algorithm (scikit-learn) but this model could be challenged with other interesting models such as XGBRegressor, Neural Networks, ...

Parameters Choice : We did not perform any hyperparameter optimisation and chose to use n_estimators=50. Future works should be planned to perform gridsearch optimizations

Metrics : Mean Squared Error metric

Validation Strategy : We splitted our data into train (75%) and test (25%)

Path To Script : https://github.com/MAIF/shapash/tree/master/tutorial/

---

Model information

Model used : RandomForestRegressor

Library : sklearn.ensemble._forest

Library version : 0.24.1

Model parameters :

Parameter key	Parameter value
base_estimator	DecisionTreeRegressor()
n_estimators	50
estimator_params	('criterion', 'max_depth', 'min_samples_split', 'min_samples_leaf', 'min_weight_fraction_leaf', 'max_features', 'max_leaf_nodes', 'min_impurity_decrease', 'min_impurity_split', 'random_state', 'ccp_alpha')
bootstrap	True
oob_score	False
n_jobs	None
random_state	None
verbose	0
warm_start	False
class_weight	None
max_samples	None
criterion	mse
max_depth	None

Parameter key	Parameter value
min_samples_split	2
min_samples_leaf	1
min_weight_fraction_leaf	0.0
max_features	auto
max_leaf_nodes	None
min_impurity_decrease	0.0
min_impurity_split	None
ccp_alpha	0.0
n_features_in_	72
n_features_	72
n_outputs_	1
base_estimator_	DecisionTreeRegressor()
estimators_	[DecisionTreeRegressor(max_features='auto', random_state=794328667), DecisionTreeRegressor(max_features='auto', random_state=829974529), DecisionTreeRegressor(max_features='auto', random_state=162270107), DecisionTreeRegressor(max_features='auto', random_state=1992995699),...

---

Dataset analysis

Global analysis

	Training dataset	Prediction dataset
number of features	72	72
number of observations	1,095	365
missing values	0	0
% missing values	0	0

Univariate analysis

INFO:numexpr.utils:NumExpr defaulting to 8 threads.

Feature

• $Value of miscellaneous feature
• Basement full bathrooms
• Basement half bathrooms
• Bedrooms above grade
• Building Class
• Central air conditioning
• Condition of sale
• Electrical system
• Enclosed porch area in square feet
• Exterior covering on house
• Exterior materials' condition
• Exterior materials' quality
• First Floor square feet
• Flatness of the property
• Full bathrooms above grade
• Garage condition
• Garage location
• Garage quality
• General condition of the basement
• General shape of property
• General zoning classification
• Ground living area square feet
• Half baths above grade
• Heating quality and condition
• Height of the basement
• Home functionality
• Interior finish of the garage?
• Kitchen quality
• Kitchens above grade
• Lot configuration
• Lot size square feet
• Low quality finished square feet
• Masonry veneer area in square feet
• Masonry veneer type
• Month Sold
• Number of fireplaces
• Open porch area in square feet
• Original construction date
• Other exterior covering on house
• Overall condition of the house
• Overall material and finish of the house
• Paved driveway
• Physical locations within Ames city limits
• Pool area in square feet
• Proximity to other various conditions
• Proximity to various conditions
• Rating of basement finished area
• Rating of basement finished area (if present)
• Refers to walkout or garden level walls
• Remodel date
• Roof material
• Screen porch area in square feet
• Second floor square feet
• Size of garage in square feet
• Slope of property
• Style of dwelling
• Three season porch area in square feet
• Total rooms above grade
• Total square feet of basement area
• Type 1 finished square feet
• Type 2 finished square feet
• Type of dwelling
• Type of foundation
• Type of heating
• Type of road access
• Type of roof
• Type of sale
• Type of utilities available
• Unfinished square feet of basement area
• Wood deck area in square feet
• Year Sold
• Year garage was built

MiscVal - Numeric

$Value of miscellaneous feature

	Training dataset	Prediction dataset
count	1,095	365
mean	51.5	19.3
std	569	111
min	0	0
25%	0	0
50%	0	0
75%	0	0
max	15,500	1,200

BsmtFullBath - Categorical

Basement full bathrooms

	Training dataset	Prediction dataset
distinct values	4	3
missing values	0	0

BsmtHalfBath - Categorical

Basement half bathrooms

	Training dataset	Prediction dataset
distinct values	3	3
missing values	0	0

BedroomAbvGr - Categorical

Bedrooms above grade

	Training dataset	Prediction dataset
distinct values	8	7
missing values	0	0

MSSubClass - Categorical

Building Class

	Training dataset	Prediction dataset
distinct values	15	15
missing values	0	0

CentralAir - Categorical

Central air conditioning

	Training dataset	Prediction dataset
distinct values	2	2
missing values	0	0

SaleCondition - Categorical

Condition of sale

	Training dataset	Prediction dataset
distinct values	6	6
missing values	0	0

Electrical - Categorical

Electrical system

	Training dataset	Prediction dataset
distinct values	5	4
missing values	0	0

EnclosedPorch - Numeric

Enclosed porch area in square feet

	Training dataset	Prediction dataset
count	1,095	365
mean	23	18.8
std	63.2	54.5
min	0	0
25%	0	0
50%	0	0
75%	0	0
max	552	272

Exterior1st - Categorical

Exterior covering on house

	Training dataset	Prediction dataset
distinct values	14	12
missing values	0	0

ExterCond - Categorical

Exterior materials' condition

	Training dataset	Prediction dataset
distinct values	5	3
missing values	0	0

ExterQual - Categorical

Exterior materials' quality

	Training dataset	Prediction dataset
distinct values	4	4
missing values	0	0

1stFlrSF - Numeric

First Floor square feet

	Training dataset	Prediction dataset
count	1,095	365
mean	1,180	1,120
std	400	341
min	334	483
25%	886	864
50%	1,100	1,050
75%	1,420	1,320
max	4,690	2,630

LandContour - Categorical

Flatness of the property

	Training dataset	Prediction dataset
distinct values	4	4
missing values	0	0

FullBath - Categorical

Full bathrooms above grade

	Training dataset	Prediction dataset
distinct values	4	4
missing values	0	0

GarageCond - Categorical

Garage condition

	Training dataset	Prediction dataset
distinct values	5	4
missing values	0	0

GarageType - Categorical

Garage location

	Training dataset	Prediction dataset
distinct values	6	6
missing values	0	0

GarageQual - Categorical

Garage quality

	Training dataset	Prediction dataset
distinct values	5	4
missing values	0	0

BsmtCond - Categorical

General condition of the basement

	Training dataset	Prediction dataset
distinct values	4	4
missing values	0	0

LotShape - Categorical

General shape of property

	Training dataset	Prediction dataset
distinct values	4	4
missing values	0	0

MSZoning - Categorical

General zoning classification

	Training dataset	Prediction dataset
distinct values	5	5
missing values	0	0

GrLivArea - Numeric

Ground living area square feet

	Training dataset	Prediction dataset
count	1,095	365
mean	1,520	1,490
std	530	512
min	334	605
25%	1,130	1,130
50%	1,470	1,460
75%	1,790	1,730
max	5,640	4,480

HalfBath - Categorical

Half baths above grade

	Training dataset	Prediction dataset
distinct values	3	3
missing values	0	0

HeatingQC - Categorical

Heating quality and condition

	Training dataset	Prediction dataset
distinct values	4	5
missing values	0	0

BsmtQual - Categorical

Height of the basement

	Training dataset	Prediction dataset
distinct values	4	4
missing values	0	0

Functional - Categorical

Home functionality

	Training dataset	Prediction dataset
distinct values	7	5
missing values	0	0

GarageFinish - Categorical

Interior finish of the garage?

	Training dataset	Prediction dataset
distinct values	3	3
missing values	0	0

KitchenQual - Categorical

Kitchen quality

	Training dataset	Prediction dataset
distinct values	4	4
missing values	0	0

KitchenAbvGr - Categorical

Kitchens above grade

	Training dataset	Prediction dataset
distinct values	3	3
missing values	0	0

LotConfig - Categorical

Lot configuration

	Training dataset	Prediction dataset
distinct values	5	5
missing values	0	0

LotArea - Numeric

Lot size square feet

	Training dataset	Prediction dataset
count	1,095	365
mean	10,600	10,300
std	10,200	9,410
min	1,300	1,600
25%	7,500	7,740
50%	9,500	9,300
75%	11,600	11,500
max	215,000	165,000

LowQualFinSF - Numeric

Low quality finished square feet

	Training dataset	Prediction dataset
count	1,095	365
mean	5.18	7.83
std	46.4	54.7
min	0	0
25%	0	0
50%	0	0
75%	0	0
max	572	513

MasVnrArea - Numeric

Masonry veneer area in square feet

	Training dataset	Prediction dataset
count	1,095	365
mean	101	109
std	173	203
min	0	0
25%	0	0
50%	0	0
75%	168	145
max	1,600	1,380

MasVnrType - Categorical

Masonry veneer type

	Training dataset	Prediction dataset
distinct values	4	4
missing values	0	0

MoSold - Categorical

Month Sold

	Training dataset	Prediction dataset
distinct values	12	12
missing values	0	0

Fireplaces - Categorical

Number of fireplaces

	Training dataset	Prediction dataset
distinct values	4	4
missing values	0	0

OpenPorchSF - Numeric

Open porch area in square feet

	Training dataset	Prediction dataset
count	1,095	365
mean	46.9	46.1
std	67.6	62.1
min	0	0
25%	0	0
50%	26	24
75%	66	72
max	547	341

YearBuilt - Numeric

Original construction date

	Training dataset	Prediction dataset
count	1,095	365
mean	1,970	1,970
std	30.3	29.8
min	1,870	1,880
25%	1,950	1,950
50%	1,970	1,970
75%	2,000	2,000
max	2,010	2,010

Exterior2nd - Categorical

Other exterior covering on house

	Training dataset	Prediction dataset
distinct values	16	14
missing values	0	0

OverallCond - Categorical

Overall condition of the house

	Training dataset	Prediction dataset
distinct values	8	9
missing values	0	0

OverallQual - Categorical

Overall material and finish of the house

	Training dataset	Prediction dataset
distinct values	10	10
missing values	0	0

PavedDrive - Categorical

Paved driveway

	Training dataset	Prediction dataset
distinct values	3	3
missing values	0	0

Neighborhood - Categorical

Physical locations within Ames city limits

	Training dataset	Prediction dataset
distinct values	25	25
missing values	0	0

PoolArea - Categorical

Pool area in square feet

	Training dataset	Prediction dataset
distinct values	6	3
missing values	0	0

Condition2 - Categorical

Proximity to other various conditions

	Training dataset	Prediction dataset
distinct values	7	3
missing values	0	0

Condition1 - Categorical

Proximity to various conditions

	Training dataset	Prediction dataset
distinct values	9	8
missing values	0	0

BsmtFinType1 - Categorical

Rating of basement finished area

	Training dataset	Prediction dataset
distinct values	6	6
missing values	0	0

BsmtFinType2 - Categorical

Rating of basement finished area (if present)

	Training dataset	Prediction dataset
distinct values	6	6
missing values	0	0

BsmtExposure - Categorical

Refers to walkout or garden level walls

	Training dataset	Prediction dataset
distinct values	4	4
missing values	0	0

YearRemodAdd - Numeric

Remodel date

	Training dataset	Prediction dataset
count	1,095	365
mean	1,990	1,980
std	20.6	20.7
min	1,950	1,950
25%	1,970	1,960
50%	1,990	1,990
75%	2,000	2,000
max	2,010	2,010

RoofMatl - Categorical

Roof material

	Training dataset	Prediction dataset
distinct values	7	5
missing values	0	0

ScreenPorch - Numeric

Screen porch area in square feet

	Training dataset	Prediction dataset
count	1,095	365
mean	16.4	11
std	58	48.3
min	0	0
25%	0	0
50%	0	0
75%	0	0
max	480	396

2ndFlrSF - Numeric

Second floor square feet

	Training dataset	Prediction dataset
count	1,095	365
mean	342	362
std	435	442
min	0	0
25%	0	0
50%	0	0
75%	728	728
max	1,870	2,060

GarageArea - Numeric

Size of garage in square feet

	Training dataset	Prediction dataset
count	1,095	365
mean	475	466
std	211	222
min	0	0
25%	329	336
50%	480	466
75%	576	576
max	1,420	1,390

LandSlope - Categorical

Slope of property

	Training dataset	Prediction dataset
distinct values	3	3
missing values	0	0

HouseStyle - Categorical

Style of dwelling

	Training dataset	Prediction dataset
distinct values	8	8
missing values	0	0

3SsnPorch - Numeric

Three season porch area in square feet

	Training dataset	Prediction dataset
count	1,095	365
mean	3.72	2.48
std	31.6	21.1
min	0	0
25%	0	0
50%	0	0
75%	0	0
max	508	245

TotRmsAbvGrd - Categorical

Total rooms above grade

	Training dataset	Prediction dataset
distinct values	12	10
missing values	0	0

TotalBsmtSF - Numeric

Total square feet of basement area

	Training dataset	Prediction dataset
count	1,095	365
mean	1,070	1,030
std	453	392
min	0	0
25%	799	780
50%	996	972
75%	1,320	1,240
max	6,110	2,630

BsmtFinSF1 - Numeric

Type 1 finished square feet

	Training dataset	Prediction dataset
count	1,095	365
mean	453	416
std	465	429
min	0	0
25%	0	0
50%	399	350
75%	722	678
max	5,640	2,100

BsmtFinSF2 - Numeric

Type 2 finished square feet

	Training dataset	Prediction dataset
count	1,095	365
mean	44.7	52
std	162	159
min	0	0
25%	0	0
50%	0	0
75%	0	0
max	1,470	1,060

BldgType - Categorical

Type of dwelling

	Training dataset	Prediction dataset
distinct values	5	5
missing values	0	0

Foundation - Categorical

Type of foundation

	Training dataset	Prediction dataset
distinct values	6	5
missing values	0	0

Heating - Categorical

Type of heating

	Training dataset	Prediction dataset
distinct values	6	3
missing values	0	0

Street - Categorical

Type of road access

	Training dataset	Prediction dataset
distinct values	2	2
missing values	0	0

RoofStyle - Categorical

Type of roof

	Training dataset	Prediction dataset
distinct values	6	6
missing values	0	0

SaleType - Categorical

Type of sale

	Training dataset	Prediction dataset
distinct values	9	8
missing values	0	0

Utilities - Categorical

Type of utilities available

	Training dataset	Prediction dataset
distinct values	2	1
missing values	0	0

BsmtUnfSF - Numeric

Unfinished square feet of basement area

	Training dataset	Prediction dataset
count	1,095	365
mean	570	558
std	446	429
min	0	0
25%	224	217
50%	483	464
75%	812	796
max	2,340	2,040

WoodDeckSF - Numeric

Wood deck area in square feet

	Training dataset	Prediction dataset
count	1,095	365
mean	96	89
std	124	131
min	0	0
25%	0	0
50%	0	0
75%	168	164
max	736	857

YrSold - Categorical

Year Sold

	Training dataset	Prediction dataset
distinct values	5	5
missing values	0	0

GarageYrBlt - Numeric

Year garage was built

	Training dataset	Prediction dataset
count	1,095	365
mean	1,980	1,980
std	26.6	25.5
min	1,870	1,900
25%	1,960	1,960
50%	1,980	1,980
75%	2,000	2,000
max	2,010	2,010

Target analysis

SalePrice - Numeric

	Training dataset	Prediction dataset
count	1,095	365
mean	182,000	177,000
std	78,500	82,000
min	34,900	40,000
25%	130,000	126,000
50%	165,000	160,000
75%	215,000	205,000
max	755,000	745,000

Multivariate analysis

---

Model explainability

Note : the explainability graphs were generated using the test set only.

Global feature importance plot

Feature

• $Value of miscellaneous feature
• Basement full bathrooms
• Basement half bathrooms
• Bedrooms above grade
• Building Class
• Central air conditioning
• Condition of sale
• Electrical system
• Enclosed porch area in square feet
• Exterior covering on house
• Exterior materials' condition
• Exterior materials' quality
• First Floor square feet
• Flatness of the property
• Full bathrooms above grade
• Garage condition
• Garage location
• Garage quality
• General condition of the basement
• General shape of property
• General zoning classification
• Ground living area square feet
• Half baths above grade
• Heating quality and condition
• Height of the basement
• Home functionality
• Interior finish of the garage?
• Kitchen quality
• Kitchens above grade
• Lot configuration
• Lot size square feet
• Low quality finished square feet
• Masonry veneer area in square feet
• Masonry veneer type
• Month Sold
• Number of fireplaces
• Open porch area in square feet
• Original construction date
• Other exterior covering on house
• Overall condition of the house
• Overall material and finish of the house
• Paved driveway
• Physical locations within Ames city limits
• Pool area in square feet
• Proximity to other various conditions
• Proximity to various conditions
• Rating of basement finished area
• Rating of basement finished area (if present)
• Refers to walkout or garden level walls
• Remodel date
• Roof material
• Screen porch area in square feet
• Second floor square feet
• Size of garage in square feet
• Slope of property
• Style of dwelling
• Three season porch area in square feet
• Total rooms above grade
• Total square feet of basement area
• Type 1 finished square feet
• Type 2 finished square feet
• Type of dwelling
• Type of foundation
• Type of heating
• Type of road access
• Type of roof
• Type of sale
• Type of utilities available
• Unfinished square feet of basement area
• Wood deck area in square feet
• Year Sold
• Year garage was built

Features contribution plots

MiscVal -

$Value of miscellaneous feature

BsmtFullBath -

Basement full bathrooms

BsmtHalfBath -

Basement half bathrooms

BedroomAbvGr -

Bedrooms above grade

MSSubClass -

Building Class

CentralAir -

Central air conditioning

SaleCondition -

Condition of sale

Electrical -

Electrical system

EnclosedPorch -

Enclosed porch area in square feet

Exterior1st -

Exterior covering on house

ExterCond -

Exterior materials' condition

ExterQual -

Exterior materials' quality

1stFlrSF -

First Floor square feet

LandContour -

Flatness of the property

FullBath -

Full bathrooms above grade

GarageCond -

Garage condition

GarageType -

Garage location

GarageQual -

Garage quality

BsmtCond -

General condition of the basement

LotShape -

General shape of property

MSZoning -

General zoning classification

GrLivArea -

Ground living area square feet

HalfBath -

Half baths above grade

HeatingQC -

Heating quality and condition

BsmtQual -

Height of the basement

Functional -

Home functionality

GarageFinish -

Interior finish of the garage?

KitchenQual -

Kitchen quality

KitchenAbvGr -

Kitchens above grade

LotConfig -

Lot configuration

LotArea -

Lot size square feet

LowQualFinSF -

Low quality finished square feet

MasVnrArea -

Masonry veneer area in square feet

MasVnrType -

Masonry veneer type

MoSold -

Month Sold

Fireplaces -

Number of fireplaces

OpenPorchSF -

Open porch area in square feet

YearBuilt -

Original construction date

Exterior2nd -

Other exterior covering on house

OverallCond -

Overall condition of the house

OverallQual -

Overall material and finish of the house

PavedDrive -

Paved driveway

Neighborhood -

Physical locations within Ames city limits

PoolArea -

Pool area in square feet

Condition2 -

Proximity to other various conditions

Condition1 -

Proximity to various conditions

BsmtFinType1 -

Rating of basement finished area

BsmtFinType2 -

Rating of basement finished area (if present)

BsmtExposure -

Refers to walkout or garden level walls

YearRemodAdd -

Remodel date

RoofMatl -

Roof material

ScreenPorch -

Screen porch area in square feet

2ndFlrSF -

Second floor square feet

GarageArea -

Size of garage in square feet

LandSlope -

Slope of property

HouseStyle -

Style of dwelling

3SsnPorch -

Three season porch area in square feet

TotRmsAbvGrd -

Total rooms above grade

TotalBsmtSF -

Total square feet of basement area

BsmtFinSF1 -

Type 1 finished square feet

BsmtFinSF2 -

Type 2 finished square feet

BldgType -

Type of dwelling

Foundation -

Type of foundation

Heating -

Type of heating

Street -

Type of road access

RoofStyle -

Type of roof

SaleType -

Type of sale

Utilities -

Type of utilities available

BsmtUnfSF -

Unfinished square feet of basement area

WoodDeckSF -

Wood deck area in square feet

YrSold -

Year Sold

GarageYrBlt -

Year garage was built

---

Model performance - Validation Set

Univariate analysis of target variable

SalePrice - Numeric

	True values	Prediction values
count	365	365
mean	177,000	177,000
std	82,000	69,300
min	40,000	63,800
25%	126,000	128,000
50%	160,000	159,000
75%	205,000	198,000
max	745,000	536,000

Metrics

Mean absolute error : 16,600

Mean squared error : 632,000,000

---

The graph below represents y_pred vs y_test :

CONFUSION MATRIX

You can add as many graphs, text, or other cells as you want.

The code will not be displayed. Only the markdown and output of the cells will be shown on the generated html file.

Stability Index

The Stability Index for  1stFlrSF :  0.054006641424508166
The Stability Index for  2ndFlrSF :  0.034436569164320176
The Stability Index for  3SsnPorch :  0.01719784169035784
The Stability Index for  BedroomAbvGr :  0.013789862414340312
The Stability Index for  BldgType :  0.3824328672343406
The Stability Index for  BsmtCond :  5.549098486292586
The Stability Index for  BsmtExposure :  3.540575124929754
The Stability Index for  BsmtFinSF1 :  0.04722680551466319
The Stability Index for  BsmtFinSF2 :  0.08937360519076179
The Stability Index for  BsmtFinType1 :  1.7406433687743188
The Stability Index for  BsmtFinType2 :  5.747471358530822
The Stability Index for  BsmtFullBath :  0.01948428358867021
The Stability Index for  BsmtHalfBath :  0.0030476803573412903
The Stability Index for  BsmtQual :  2.2089222827238193
The Stability Index for  BsmtUnfSF :  0.00992635348082776
The Stability Index for  CentralAir :  0.48817432906301794
The Stability Index for  Condition1 :  8.640895979688906
The Stability Index for  Condition2 :  12.747415809814878
The Stability Index for  Electrical :  11.504811832067588
The Stability Index for  EnclosedPorch :  0.018283232452415286
The Stability Index for  ExterCond :  16.242794802790844
The Stability Index for  ExterQual :  9.106108224770106
The Stability Index for  Exterior1st :  5.698360880459419
The Stability Index for  Exterior2nd :  4.175586494079361
The Stability Index for  Fireplaces :  0.004688364174025415
The Stability Index for  Foundation :  0.7775915824743733
The Stability Index for  FullBath :  0.014307653816356028
The Stability Index for  Functional :  13.609871038041822
The Stability Index for  GarageArea :  0.042537105422203986
The Stability Index for  GarageCond :  6.944761111201195
The Stability Index for  GarageFinish :  1.9784896764330426
The Stability Index for  GarageQual :  7.70346460805724
The Stability Index for  GarageType :  8.632373874527254
The Stability Index for  GarageYrBlt :  0.07490718454298781
The Stability Index for  GrLivArea :  0.021155765014728867
The Stability Index for  HalfBath :  0.007060437470689766
The Stability Index for  Heating :  0.0004415056023075196
The Stability Index for  HeatingQC :  2.7808152198921774
The Stability Index for  HouseStyle :  3.196777792254942
The Stability Index for  KitchenAbvGr :  0.02314690705057906
The Stability Index for  KitchenQual :  2.3694295218022576
The Stability Index for  LandContour :  6.446640743539879
The Stability Index for  LandSlope :  11.232801853580499
The Stability Index for  LotArea :  0.023994823468863485
The Stability Index for  LotConfig :  5.428273089590677
The Stability Index for  LotShape :  2.8298547297403567
The Stability Index for  LowQualFinSF :  0.02556844109038931
The Stability Index for  MSSubClass :  1.516077731759784
The Stability Index for  MSZoning :  6.274363797174393
The Stability Index for  MasVnrArea :  0.0551858812946571
The Stability Index for  MasVnrType :  0.02459961911818724
The Stability Index for  MiscVal :  0.015198882072831273
The Stability Index for  MoSold :  0.012675762033250049
The Stability Index for  Neighborhood :  0.24575009307083714
The Stability Index for  OpenPorchSF :  0.028661061040918062
The Stability Index for  OverallCond :  0.061379799987294655
The Stability Index for  OverallQual :  0.03777989457191217
The Stability Index for  PavedDrive :  6.3563654192865915
The Stability Index for  PoolArea :  0.01412518652462114
The Stability Index for  RoofMatl :  12.659021939768715
The Stability Index for  RoofStyle :  1.1752678311038083
The Stability Index for  SaleCondition :  9.703485338076057
The Stability Index for  SaleType :  10.300915690282656
The Stability Index for  ScreenPorch :  0.06870060110730973
The Stability Index for  Street :  0.035828095107122156
The Stability Index for  TotRmsAbvGrd :  0.051711977207815736
The Stability Index for  TotalBsmtSF :  0.036470243512358815
The Stability Index for  Utilities :  9.209419337938986
The Stability Index for  WoodDeckSF :  0.04692981933019543
The Stability Index for  YearBuilt :  0.018847367925547868
The Stability Index for  YearRemodAdd :  0.057528132951586076
The Stability Index for  YrSold :  0.0037448436190685333